Analysis and Conversion Predictions With Machine Learning Using Decision Trees + Source code & Notebook

Hi devs!

A few weeks ago I had a client who wanted to analyze his consumer database and try to predict consumer conversions, or understand the attributes of the customers most likely to buy into his SaaS company. I asked his permission to publish part of the results, and after aninomizing the personal data of the clients, I gave myself the task of publishing these results.

Before we start, I would like to give you an overview of the database and its context. It is not a very complex database, but there some important data is stored such as the type of work the client does, marital status, education, something that could apply to any other business, since the important thing is the last column of the database: the Y column

What is the Y column and why is it so important?
The Y column can be any column in your database that contains the final result of a customer interaction, in this case the column says no for those who did not buy, and says yes for those who did. Nothing difficult, right? Everyone should have at least that column in their database. In this particular case, that column tells us if the client finally subscribed to the service or not.

It is important to note that this data is available for any type of business regardless of size, and they could do this same type of sophisticated analysis and discover with data, which is the ideal type of customer for the company! Very insightful!

Let's begin

Download the source code and notebook here: https://www.narrativetext.co/the-analyst/analysis-and-conversion-predictions-with-machine-learning-using-decision-trees